Useful measures in the field of time and dimensional rationalisation of manual training lessons

Schoolwork, especially lessons in manual skills is often associated with various ergonomics stresses. These stresses are the result not only of school obligations but also of the physical working environment and inadequate lesson planning. Much can be done in this field if certain approaches are taken into consideration at the work planning stage. Thus we can use certain analytical methods, such as the Method of Temporary Observations (Multi-Moment Method) and OWAS (a method for the evaluation of postural load during work), when positioning stressful lesson factors. This can be done in a relatively simple way in the field of time rationalisation as well as in the field of dimensional work adjustment to pupils. Research results in this paper show that by using a planned directed approach in this field, it is possible to achieve, a higher level of efficiency with normal levels of fatigue.     

The conversion process in the laws of Russia

Conclusion To sum up, the process of conversion is taking place in favourable external conditions. Disarmament has become the imperative of our times. For this reason Russian legislation considers not only world achievements and mistakes in this field, but also opens great opportunities to international cooperation in all possible forms and spheres of action.The transformation of agricultural production, medical equipment, the energy industry, communications, ecology, space, and the production of consumer goods, make up just a small part of the tasks to which converting enterprises, in a spirit of international cooperation, can direct their efforts.The dimensions of the reconstruction of the Russian industry are enormous. And it is to this task that all those who have a clear mind and energy, all those who are capable of creativity and initiative, should apply their energy. The aim of Russian legislators should be to create favourable conditions for those who wish to give their contribution to this great task.     

Forecasting European high-growth Firms - A Random Forest Approach

High-growth firms (HGFs) have aroused considerable interest both by researchers and policymakers mainly because of their substantial contribution to job creation and to the advancement of the surrounding economy (Acs et al., Small Bus Res Summ (328):1–92 2008, Schreyer 2000). Any initiative to foster HGFs requires the ability to reliably anticipate them. There seems to be a consensus in previous mainly regression-based studies on the impossibility of such a prediction (Coad, Doc Trav Centre d’Econ Sorbonne 24:1–72 2007b). Using a novel random forest (RF) based approach and a recent data set (2004–2014) covering 179970 unique firms from nine European countries, we show the potential of a true out-of-sample prediction: depending on the country, we were able to determine up to 39% of all HGFs by selecting only ten percent of all firms. The RF algorithm is both used to determine relevant predictors and for the actual prediction and pattern analysis. Both the selection of the best RF and the cross-country comparisons are based on a Receiver Operating Characteristic analysis. We find that most accurate HGF predictions are possible in GB, France, and Italy and largely confirm this ranking using Venkatraman’s unpaired test. Apart from the firm’s size, age, and past growth, the sales per employee, the fixed assets ratio, and the debt ratio are quite important. Our “typical” HGFs determined using RF prototypes have been older and bigger than the remaining firms, which is counterintuitive and atypical in literature. Based on our finding, typical HGFs are not start-ups, which questions current political funding strategies. Apart from that, our results do not support and rather refute the existence of a survivorship bias. Moreover, approximately every fourth HGF remains to be a HGF in the next period.